Abstract:

In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements and establishing associated scheduling throughout a given supply chain system.This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications.The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.